论文标题
利用神经模型中的相干性和句法特征的潜力进行自动评分
Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring
论文作者
论文摘要
在自动论文评分的迅速特定的整体得分预测任务中，一般方法包括预先训练的神经模型，相干模型和混合模型，将句法特征与神经模型结合在一起。在本文中，我们提出了一种新颖的方法来提取和代表迅速学习的NSP的论文连贯性特征，该特征显示以匹配最先进的AES连贯模型，并实现长篇文章的最佳性能。我们将句法特征致密嵌入嵌入以增强基于BERT的模型，并为AES的混合方法提供最佳性能。此外，我们还探索了各种想法，以结合一致性，句法信息和语义嵌入，这是以前从未做过的。我们的组合模型的性能也比可用于组合模型的SOTA更好，即使它的表现并不优于我们的句法增强神经模型。我们进一步提供对未来研究有用的分析。
In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to match the state-of-the-art AES coherence model, and achieves the best performance for long essays. We apply syntactic feature dense embedding to augment BERT-based model and achieve the best performance for hybrid methodology for AES. In addition, we explore various ideas to combine coherence, syntactic information and semantic embeddings, which no previous study has done before. Our combined model also performs better than the SOTA available for combined model, even though it does not outperform our syntactic enhanced neural model. We further offer analyses that can be useful for future study.
